



import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict






@pytest.mark.parametrize(
    "files"							,					[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]							,					)
def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Dict					  =       tmp_path_factory.mktemp("dset_infos_dir"    )
							if "full:README.md" in files:
														with open(dataset_infos_dir / "README.md"							,					"w"    ) as f:
																					f.write("---\ndataset_info:\n  dataset_size: 42\n---"    )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / "README.md"							,					"w"    ) as f:
																					f.write(""    )
														# we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / "dataset_infos.json"							,					"w"    ) as f:
																					f.write("{\"default\": {\"dataset_size\": 42}}"    )
							_lowerCAmelCase	:				int					  =       DatasetInfosDict.from_directory(a_    )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42






@pytest.mark.parametrize(
    "dataset_info"							,					[
        DatasetInfo(),
        DatasetInfo(
            description="foo"							,					features=Features({"a": Value("int32"    )}    )							,					builder_name="builder"							,					config_name="config"							,					version="1.0.0"							,					splits=[{"name": "train"}]							,					download_size=42							,					),
    ]							,					)
def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[Any]					  =       str(a_    )
							dataset_info.write_to_directory(a_    )
							_lowerCAmelCase	:				int					  =       DatasetInfo.from_directory(a_    )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(a_							,					"dataset_info.json"    )    )






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				Optional[Any]					  =       DatasetInfo(
							    description="foo"							,					citation="bar"							,					homepage="https://foo.bar"							,					license="CC0"							,					features=Features({"a": Value("int32"    )}    )							,					post_processed={}							,					supervised_keys=()							,					task_templates=[]							,					builder_name="builder"							,					config_name="config"							,					version="1.0.0"							,					splits=[{"name": "train", "num_examples": 42}]							,					download_checksums={}							,					download_size=1_337							,					post_processing_size=442							,					dataset_size=1_234							,					size_in_bytes=1_337 + 442 + 1_234							,					)
							_lowerCAmelCase	:				Dict					  =       dataset_info._to_yaml_dict()
							assert sorted(a_    ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML    )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]							,					(list, dict, int, str)    )
							_lowerCAmelCase	:				Tuple					  =       yaml.safe_dump(a_    )
							_lowerCAmelCase	:				List[str]					  =       yaml.safe_load(a_    )
							assert dataset_info_yaml_dict == reloaded






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				Tuple					  =       DatasetInfo()
							_lowerCAmelCase	:				Dict					  =       dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"							,					[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}    ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}    ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"							,					features=Features({"a": Value("int32"    )}    )							,					builder_name="builder"							,					config_name="config"							,					version="1.0.0"							,					splits=[{"name": "train"}]							,					download_size=42							,					)
            }    ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42    ),
                "v2": DatasetInfo(dataset_size=1_337    ),
            }    ),
    ]							,					)
def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				List[str]					  =       str(a_    )
							dataset_infos_dict.write_to_directory(a_    )
							_lowerCAmelCase	:				Optional[Any]					  =       DatasetInfosDict.from_directory(a_    )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														_lowerCAmelCase	:				str					  =       config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														_lowerCAmelCase	:				Any					  =       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()    )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(a_							,					"README.md"    )    )






'''simple docstring'''


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_					=   Lock()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple					,       a_						:				Dict					,       a_						:				Optional[Any]					,       a_						:				str					,       a_						:				Any					,       a_						:				Optional[Any]      ):
  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0					,       10      ):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(a_      )
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      __a								=       rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      __a								=       min(a_					,       a_      )
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(a_      )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      __a								=       lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      __a								=       max(a_					,       a_      )
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       []
  __a								=       []
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()      )
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  __a								=       Pipe()
  __a								=       Pipe()
  process_array_.append(
      Process(
          target=a_					,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,       )      )
  __a								=       temp_rs
  __a								=       temp_rr

  for i in range(1					,       len(a_      ) - 1      ):
    __a								=       Pipe()
    __a								=       Pipe()
    process_array_.append(
        Process(
            target=a_					,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,       )      )
    __a								=       temp_rs
    __a								=       temp_rr

  process_array_.append(
      Process(
          target=a_					,       args=(
              len(a_      ) - 1,
              arr[len(a_      ) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(a_      ) - 1],
          )					,       )      )

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0					,       len(a_      )      ):
    __a								=       result_pipe[p][0].recv()
    process_array_[p].join()
  return arr


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       list(range(10					,       0					,       -1      )      )
  print('Initial List'      )
  print(*a_      )
  __a								=       odd_even_transposition(a_      )
  print('Sorted List\n'      )
  print(*a_      )


if __name__ == "__main__":
  main()



